"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/quick_start.md" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

quick_start.md  (prophet-1.1):quick_start.md  (prophet-1.1.1)
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First we'll import the data: First we'll import the data:
```python ```python
# Python # Python
import pandas as pd import pandas as pd
from prophet import Prophet from prophet import Prophet
``` ```
```python ```python
# Python # Python
df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv') df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/exampl es/example_wp_log_peyton_manning.csv')
df.head() df.head()
``` ```
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library(prophet) library(prophet)
``` ```
R[write to console]: Loading required package: Rcpp R[write to console]: Loading required package: Rcpp
R[write to console]: Loading required package: rlang R[write to console]: Loading required package: rlang
First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/main/examples/example_wp_log_peyton_manning.csv). First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/main/examples/example_wp_log_peyton_manning.csv).
```R ```R
# R # R
df <- read.csv('../examples/example_wp_log_peyton_manning.csv') df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples /example_wp_log_peyton_manning.csv')
``` ```
We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation. We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation.
```R ```R
# R # R
m <- prophet(df) m <- prophet(df)
``` ```
Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable datafr ame. By default it will also include the historical dates so we can evaluate in- sample fit. Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable datafr ame. By default it will also include the historical dates so we can evaluate in- sample fit.
```R ```R
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